{"id":"W4319082079","doi":"10.26685/urncst.426","title":"Feasibility Study: Machine Learning in Neurodegenerative Disorders, Alzheimer’s Disease","year":2023,"lang":"en","type":"article","venue":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Machine learning; Logistic regression; Medical diagnosis; Binary classification; Computer science; Clinical decision support system; Support vector machine; Medicine; Decision support system; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","research_integrity"],"consensus_categories":["sts"],"category_scores_codex":[0.02175756,0.0001840755,0.0004069846,0.001623686,0.003303265,0.00008090261,0.0006453079,0.0002632627,0.00002008223],"category_scores_gemma":[0.01301164,0.0001321072,0.00004266324,0.006241404,0.003112723,0.0004127708,0.001075234,0.009067531,0.00006704623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002700675,"about_ca_system_score_gemma":0.001362142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056889,"about_ca_topic_score_gemma":0.01656136,"domain_scores_codex":[0.9925716,0.002873638,0.001160531,0.0008718692,0.001050293,0.001472054],"domain_scores_gemma":[0.995361,0.002876165,0.0001930755,0.0002888853,0.0006909583,0.0005898666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001455127,0.0002107814,0.9416575,0.00001639403,0.00000699195,0.000155629,0.0002631974,0.00001065776,0.00003131529,0.002072389,0.0001246261,0.05530496],"study_design_scores_gemma":[0.0009677224,0.0009498856,0.6930065,0.0002121227,0.00001005953,0.00001136632,0.01289171,0.03291025,0.000008437825,0.258176,0.0006388974,0.0002171239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8684097,0.001847855,0.000001922593,0.1280449,0.0004585028,0.001048979,0.000002978268,0.00008539214,0.00009977228],"genre_scores_gemma":[0.9900366,0.009422246,0.00003124063,0.0001452436,0.0001151139,0.00005121083,0.000001904873,0.00001220691,0.0001842531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2561036,"threshold_uncertainty_score":0.9996002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.336305018328938,"score_gpt":0.5862845611743495,"score_spread":0.2499795428454115,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}